{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 4 - Policy Gradient Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import gymnasium as gym\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def running_mean(x, N=50):\n",
    "    kernel = np.ones(N)\n",
    "    conv_len = x.shape[0]-N\n",
    "    y = np.zeros(conv_len)\n",
    "    for i in range(conv_len):\n",
    "        y[i] = kernel @ x[i:i+N]\n",
    "        y[i] /= N\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = gym.make(\"CartPole-v1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Listing 4.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "l1 = 4 #A\n",
    "l2 = 150\n",
    "l3 = 2 #B\n",
    "\n",
    "model = torch.nn.Sequential(\n",
    "    torch.nn.Linear(l1, l2),\n",
    "    torch.nn.LeakyReLU(),\n",
    "    torch.nn.Linear(l2, l3),\n",
    "    torch.nn.Softmax(dim=0) #C\n",
    ")\n",
    "\n",
    "learning_rate = 0.009\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "#A Input data is length 4\n",
    "#B Output is a 2-length vector for the Left and the Right actions\n",
    "#C Output is a softmax probability distribution over actions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Listing 4.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "state1 = env.reset()\n",
    "pred = model(torch.from_numpy(state1[0])) #G\n",
    "action = np.random.choice(np.array([0,1]), p=pred.data.numpy()) #H\n",
    "state2, reward, done, info, _ = env.step(action) #I\n",
    "\n",
    "#G Call policy network model to produce predicted action probabilities\n",
    "#H Sample an action from the probability distribution produced by the policy network\n",
    "#I Take the action, receive new state and reward. The info variable is produced by the environment but is irrelevant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Listing 4.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discount_rewards(rewards, gamma=0.99):\n",
    "    lenr = len(rewards)\n",
    "    disc_return = torch.pow(gamma,torch.arange(lenr).float()) * rewards #A\n",
    "    disc_return /= disc_return.max() #B\n",
    "    return disc_return\n",
    "\n",
    "#A Compute exponentially decaying rewards\n",
    "#B Normalize the rewards to be within the [0,1] interval to improve numerical stability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Listing 4.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_fn(preds, r): #A\n",
    "    return -1 * torch.sum(r * torch.log(preds)) #B\n",
    "\n",
    "#A The loss function expects an array of action probabilities for the actions that were taken and the discounted rewards.\n",
    "#B It computes the log of the probabilities, multiplies by the discounted rewards, sums them all and flips the sign."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Listing 4.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_DUR = 200\n",
    "MAX_EPISODES = 500\n",
    "gamma = 0.99\n",
    "score = [] #A\n",
    "expectation = 0.0\n",
    "for episode in range(MAX_EPISODES):\n",
    "    curr_state = env.reset()[0]\n",
    "    done = False\n",
    "    transitions = [] #B\n",
    "    \n",
    "    for t in range(MAX_DUR): #C\n",
    "        act_prob = model(torch.from_numpy(curr_state).float()) #D\n",
    "        action = np.random.choice(np.array([0,1]), p=act_prob.data.numpy()) #E\n",
    "        prev_state = curr_state\n",
    "        curr_state, _, done, _, info = env.step(action) #F\n",
    "        transitions.append((prev_state, action, t+1)) #G\n",
    "        if done: #H\n",
    "            break\n",
    "\n",
    "    ep_len = len(transitions) #I\n",
    "    score.append(ep_len)\n",
    "    reward_batch = torch.Tensor([r for (s,a,r) in transitions]).flip(dims=(0,)) #J\n",
    "    disc_returns = discount_rewards(reward_batch) #K\n",
    "    state_batch = torch.Tensor([s for (s,a,r) in transitions]) #L\n",
    "    action_batch = torch.Tensor([a for (s,a,r) in transitions]) #M\n",
    "    pred_batch = model(state_batch) #N\n",
    "    prob_batch = pred_batch.gather(dim=1,index=action_batch.long().view(-1,1)).squeeze() #O\n",
    "    loss = loss_fn(prob_batch, disc_returns)\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "#A List to keep track of the episode length over training time\n",
    "#B List of state, action, rewards (but we ignore the reward)\n",
    "#C While in episode\n",
    "#D Get the action probabilities\n",
    "#E Select an action stochastically\n",
    "#F Take the action in the environment\n",
    "#G Store this transition\n",
    "#H If game is lost, break out of the loop\n",
    "#I Store the episode length\n",
    "#J Collect all the rewards in the episode in a single tensor\n",
    "#K Compute the discounted version of the rewards\n",
    "#L Collect the states in the episode in a single tensor\n",
    "#M Collect the actions in the episode in a single tensor\n",
    "#N Re-compute the action probabilities for all the states in the episode\n",
    "#O Subset the action-probabilities associated with the actions that were actually taken "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = np.array(score)\n",
    "avg_score = running_mean(score, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5dcc089420>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "plt.ylabel(\"Episode Duration\",fontsize=22)\n",
    "plt.xlabel(\"Training Epochs\",fontsize=22)\n",
    "plt.plot(avg_score, color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Listing 4.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = []\n",
    "games = 100\n",
    "done = False\n",
    "state1 = env.reset()[0]\n",
    "for i in range(games):\n",
    "    t=0\n",
    "    while not done: #F\n",
    "        if type(state1) is tuple:\n",
    "            state1 = state1[0]\n",
    "        pred = model(torch.from_numpy(state1).float()) #G\n",
    "        action = np.random.choice(np.array([0,1]), p=pred.data.numpy()) #H\n",
    "        state2, reward, done, _, info = env.step(action) #I\n",
    "        state1 = state2\n",
    "        if(type(state1) == 'tuple'):\n",
    "            state1 = state2[0]\n",
    "        \n",
    "        t += 1\n",
    "        if t > MAX_DUR: #L\n",
    "            break;\n",
    "    state1 = env.reset()\n",
    "    done = False\n",
    "    score.append(t)\n",
    "score = np.array(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f5dc40efca0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(np.arange(score.shape[0]),score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
